Towards Automatic Gesture Stroke Detection

Binyam Gebrekidan Gebre, Peter Wittenburg, Przemyslaw Lenkiewicz


Abstract
Automatic annotation of gesture strokes is important for many gesture and sign language researchers. The unpredictable diversity of human gestures and video recording conditions require that we adopt a more adaptive case-by-case annotation model. In this paper, we present a work-in progress annotation model that allows a user to a) track hands/face b) extract features c) distinguish strokes from non-strokes. The hands/face tracking is done with color matching algorithms and is initialized by the user. The initialization process is supported with immediate visual feedback. Sliders are also provided to support a user-friendly adjustment of skin color ranges. After successful initialization, features related to positions, orientations and speeds of tracked hands/face are extracted using unique identifiable features (corners) from a window of frames and are used for training a learning algorithm. Our preliminary results for stroke detection under non-ideal video conditions are promising and show the potential applicability of our methodology.
Anthology ID:
L12-1240
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
231–235
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/454_Paper.pdf
DOI:
Bibkey:
Cite (ACL):
Binyam Gebrekidan Gebre, Peter Wittenburg, and Przemyslaw Lenkiewicz. 2012. Towards Automatic Gesture Stroke Detection. In Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12), pages 231–235, Istanbul, Turkey. European Language Resources Association (ELRA).
Cite (Informal):
Towards Automatic Gesture Stroke Detection (Gebre et al., LREC 2012)
Copy Citation:
PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/454_Paper.pdf